Iris code hashing

This paper proposes an indexing technique for iris database using iris codes. Iris code which is the textural features of an iris is efficiently hashed such that it reduces the complexity of searching. It has been built on the Hamming distance based Locality Sensitive Hashing that samples bits of iris code. It reduces both computational and memory costs significantly. Also, it is robust to illumination and occlusion due to eyelids and eyelashes. It has been tested on publicly available database, viz. CASIA-V3-Interval [7]. Further, it has been compared with enhanced geometric hashing [9] technique and it is found to be better in terms of its query response time.